The number of people suffering from diabetes in Taiwan has continued to rise in recent years. According to the statistics of the International Diabetes Federation, about 537 million people worldwide (10.5% of the global population) suffer from diabetes, and it is estimated that 643 million people will develop the condition (11.3% of the total population) by 2030. If this trend continues, the number will jump to 783 million (12.2%) by 2045. At present, the number of people with diabetes in Taiwan has reached 2.18 million, with an average of one in ten people suffering from the disease. In addition, according to the Bureau of National Health Insurance in Taiwan, the prevalence rate of diabetes among adults in Taiwan has reached 5% and is increasing each year. Diabetes can cause acute and chronic complications that can be fatal. Meanwhile, chronic complications can result in a variety of disabilities or organ decline. If holistic treatments and preventions are not provided to diabetic patients, it will lead to the consumption of more medical resources and a rapid decline in the quality of life of society as a whole. In this study, based on the outpatient examination data of a Taipei Municipal medical center, 15,000 women aged between 20 and 80 were selected as the subjects. These women were patients who had gone to the medical center during 2018–2020 and 2021–2022 with or without the diagnosis of diabetes. This study investigated eight different characteristics of the subjects, including the number of pregnancies, plasma glucose level, diastolic blood pressure, sebum thickness, insulin level, body mass index, diabetes pedigree function, and age. After sorting out the complete data of the patients, this study used Microsoft Machine Learning Studio to train the models of various kinds of neural networks, and the prediction results were used to compare the predictive ability of the various parameters for diabetes. Finally, this study found that after comparing the models using two-class logistic regression as well as the two-class neural network, two-class decision jungle, or two-class boosted decision tree for prediction, the best model was the two-class boosted decision tree, as its area under the curve could reach a score of 0.991, which was better than other models.
This paper proposes a novel hybrid equalizer circuit (HEC) for a battery management system (BMS) to implement the passive HEC (P-HEC), active HEC (A-HEC), or active/passive (AP-HEC) with the same equalizer circuit architecture. The advantages of an HEC are that it is simple, cost-effective, highly energy efficient, and fail safe. The P-HEC can further use a cooling fan or heater instead of a conventional resistor as a power dissipation element to convert the energy of the waste heat generated by the resistor to adjust the battery temperature. Even if the P-HEC uses the resistor to consume energy as in conventional methods, the P-HEC still dramatically improves the component lifetime and reliability of the BMS because the waste heat generated by the equalizer resistor is outside of the BMS board. Three significant advantages of an A-HEC are its (1) low cost, (2) small volume, and (3) higher energy efficiency than the conventional active equalizer circuits (AECs). In the HEC design, the MOSFETs of the switch array do not need high-speed switching to transfer energy as conventional AECs with DC/DC converter architecture because the A-HEC uses an isolated battery charger to charge the string cell. Therefore, the switch array is equal to a cell selector with a simple ON/OFF function. In summary, the HEC provides a small volume, cost-effective, high efficiency, and fail-safe equalizer circuit design to satisfy cell balancing demands for all kinds of electric vehicles (EVs) and energy storage systems (ESSs).
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